semsimian 0.2.9

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semsimian

Semsimian is a package to provide fast semantic similarity calculations for ontologies. It is a Rust library with a Python interface.

This includes implementation of Jaccard and Resnik similarity of terms in an ontology, as well as a method to calculate the similarity of two sets of terms (so-called termset similarity). Other methods will be added in the future.

Semsimian is currently integrated into OAK and the Monarch app to provide fast semantic similarity calculations.

Installation

• Set up your virtual environment of choice.
• cd semsimian (home directory of this project)
• pip install maturin
• maturin develop
• python

Python 3.9.16 (main, Jan 11 2023, 10:02:19) 
[Clang 14.0.6 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from semsimian import Semsimian
>>> s = Semsimian([('banana', 'is_a', 'fruit'), ('cherry', 'is_a', 'fruit')])
>>> s.jaccard_similarity('banana', 'cherry')

This should yield a value of 1.0.

Releases

As of version 0.1.14, the semsimian source is released on GitHub, with a corresponding set of Python wheels released to Pypi.

To trigger a new set of builds, first update the version number in Cargo.toml, then create a new release.

Wheels are prepared for the following environments and architectures:

OS	Architectures	Python Versions
Linux	x86_64, x86_64-unknown-linux-musl, aarch64-unknown-linux-gnu, aarch64-unknown-linux-musl	3.7, 3.8, 3.9, 3.10, 3.11
MacOS	x86_64, universal2	3.7, 3.8, 3.9, 3.10, 3.11
Windows	x86_64	3.7, 3.8, 3.9, 3.10, 3.11

Troubleshooting

Building for Mac ARM M1 architectures

If a import semsimian results in a ImportError warning about incompatible architecture, try the following:

• Install conda. This guide may be helpful.
• Set up a virtual environment with conda so that your Python build is aligned with your processor architecture (in this case, ARM). Try something like:

$ conda create -n myenv python=3.9
...setup happens...
$ conda activate myenv

and then proceed as above.

Code Coverage via Docker

Build a docker image:

docker build -t my-rust-app .

Run your tests inside a Docker container and generate coverage:

docker run -v "$(pwd)":/usr/src/app -t my-rust-app bash -c "CARGO_INCREMENTAL=0 RUSTFLAGS='-Zprofile -Ccodegen-units=1 -Cinline-threshold=0 -Coverflow-checks=off -Zpanic_abort_tests -Cpanic=abort' cargo test && grcov . -s . --binary-path ./target/debug/ -t html --branch --ignore-not-existing -o ./target/debug/coverage/"

Get Coverage report from:

open ./target/debug/coverage/index.html